Abstract
Anaerobic digestion (AD) across Europe and the UK faces increasing economic and operational pressure from volatile feedstock supply under climate extremes. Existing stochastic programming (SP) approaches for feedstock planning often rely on limited historical observations and/or simplify yield uncertainty in ways that miss the joint, non-linear response of crops to weather variability, thereby understating downside supply risk. We develop an integrated decision-support framework that links climate uncertainty to AD procurement planning by coupling mechanistic crop simulation, generative surrogate modelling, and stochastic optimization. First, APSIM is used offline to generate a mechanistic yield knowledge base across weather trajectories and discrete planting-density choices. Then, a conditional GAN (CGAN) is trained to produce non-parametric joint yield samples for multi crops conditioned on scenario features and management, enabling fast Monte Carlo evaluation. At last, these samples are embedded in a two-stage SP that optimizes first-stage land allocation and planting densities, with second-stage recourse represented by spot-market purchases to cover shortfall. The architecture is designed for stage-based rolling updates as forecasts are progressively replaced by observations. We demonstrate the framework on a 15-ha unit under different contracts of biogas-equivalent output. Results reveal a target-induced regime shift in optimal procurement. Under moderate production targets, monocropping solutions minimize cost with negligible loss in reliability, reflecting broad operational indifference across several land allocation patterns. As contract levels approach the biophysical limits of a 15-ha system (~110×10³ m³), the optimizer transitions into a risk-reducing regime where maize-rye double cropping becomes increasingly necessary. At high targets, the feasible set collapses toward near-complete double cropping. At a representative contract of 140×10³ m³, the least-cost rye-only plan achieves 34.3% supply confidence, while full double cropping increases confidence to 85.7% but with higher cultivation cost. Even under full intensification, an irreducible tail risk remains (14.3% shortfall frequency), implying unavoidable reliance on spot-market procurement under extreme seasons. The APSIM-CGAN-SP integration translates climate-driven biophysical uncertainty into actionable procurement strategies. It reveals threshold behavior, tail-risk exposure, and the limits of intensification under fixed land constraints. The framework supports both pre-seasons planning and rolling in-season updates, providing a quantitative basis for contract feasibility assessment, hedging design, and resilient feedstock procurement.